In the face of regulatory processes such as Basel III and Solvency 2, enterprises are becoming increasingly concerned with managing and assessing the credit, financial, engineering, and operational risk arising from uncertain data. Examples of uncertain data include future values of financial assets, customer order quantities under hypothetical price changes, and transportation times for future shipments under alternative shipping schemes.
Such uncertainty is typically modeled as a probability distribution over the uncertain data values, specified by means of a complex (often predictive) stochastic model. The probability distribution over data values leads to a probability distribution over database query results, and risk assessment amounts to exploration of the upper or lower tail of a query-result distribution.
Monte Carlo Database approaches have been proposed to permit Monte Carlo analysis of query-result distributions arising from complex data intensive stochastic models, but such systems cannot estimate extreme quantiles of such distributions nor permit estimation of properties of the distribution tails defined by such quantiles. The difficulty is that too many Monte Carlo replications are needed in order to observe extreme values of query answers.